{"id":"W4327696730","doi":"10.36227/techrxiv.21431889.v2","title":"A Smart Network Intrusion Detection System for Cyber Security of Industrial IoT","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Concordia University; Lakehead University","funders":"","keywords":"SCADA; Computer science; Intrusion detection system; Industrial control system; Deep learning; Convolutional neural network; Perceptron; Critical infrastructure; Artificial intelligence; Artificial neural network; Computer security; Real-time computing; Computer network; Control (management); Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000461943,0.0006793892,0.0005989898,0.0008685767,0.0003483616,0.0005253917,0.0006741025,0.0006477303,0.00362764],"category_scores_gemma":[0.0007774871,0.0002209236,0.0003620222,0.0005250669,0.0002305091,0.001067941,0.0005913219,0.0007260553,0.001478557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006100395,"about_ca_system_score_gemma":0.0005937769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002116316,"about_ca_topic_score_gemma":0.002803523,"domain_scores_codex":[0.9995951,0.00003909739,0.0000280842,0.0001102846,0.00017924,0.00004822711],"domain_scores_gemma":[0.9997301,0.00004538343,0.00003632046,0.00006803081,0.00009044934,0.00002967685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001850465,0.0009075252,0.01682447,0.0005554982,0.0003121475,0.0007258769,0.00015485,0.0626889,0.104248,0.007986242,0.08321182,0.7205343],"study_design_scores_gemma":[0.0001292831,0.0008191735,0.01602704,0.00005201448,0.0001092802,0.0006906743,0.00004804941,0.8458419,0.08511592,0.004056168,0.04705395,0.00005660642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3392871,0.002042006,0.5203701,0.001233761,0.001260438,0.001634864,0.01082118,0.08889566,0.03445487],"genre_scores_gemma":[0.7481197,0.0006155052,0.2171759,0.0005912401,0.000099243,0.0005241309,0.01380467,0.0004335955,0.01863606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00362764,"threshold_uncertainty_score":0.01213562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04196333825585117,"score_gpt":0.2523337334528327,"score_spread":0.2103703951969816,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}